Mobility improvement for patients is one of the primary goals of physiotherapy rehabilitation. Providing the physiotherapist and the patient with a quantified and objective measure of progress can be beneficial for monitoring the patient's condition.
The application of machine learning techniques to human motion analysis has grown rapidly in recent years. Measurement and analysis of physiotherapy data has the potential to provide an objective and quantitative measure of a patient's progress and improvement in motion performance over the course of his or her physiotherapy treatment. During a typical physiotherapy session, a physiotherapist instructs the patient to perform a number of exercises, each with several repetitions. The set of exercises chosen and the number of repetitions may be customized for each patient. The physiotherapist then evaluates the patient's progress based on their performance. As the patient's condition improves the patient's performance also improves.
In current clinical practice, the patient's condition is typically assessed using visual observation of the patient's motions, questionnaires, and goniometry. Questionnaires such as the Community Balance and Mobility Scale and the Falls Efficiency Scale are used to assign a score to the patient's motion quality. Goniometry is a technique of measuring joint angles which isolates a single body joint in order to evaluate a subject's range of motion. Goniometry is not accurate when the subject is moving e.g., during exercises and functional rehabilitation.
The current measurement and assessment techniques require physiotherapist effort and monitoring, and are not capable of measuring during movement. Automation of patient observation would support physiotherapy practice through automated assessment and evaluation of exercise performance.
An automated system could also provide the therapist with numerical metrics to assess the patient's recovery process and potentially allow physiotherapists to assess the effectiveness of various treatment protocols over a population of patients.
Patient data analysis for progress monitoring is a challenging task because of the complexity of human motion. Human movement consists of synchronous recruitment of multiple degrees of freedom (DoF), making single DoF comparisons (e.g., only comparing the range of motion in one joint) incomplete and possibly unreliable. Furthermore there are many sources of variability in human motion data.
For a single individual performing several repetitions of the same movement, there is both spatial and temporal variability. This variability is due to the nature of human movement, each repetition of the same exercise will be different, due to the stochastic nature of muscle recruitment. Interpersonal variability is due to differences in the physical characteristics of different individuals, such as differences in height, weight, fitness level, etc. The measurement system, such as sensor noise and a gyroscope drift cause variability in the patient motion data. Initial pose and sensor positioning could introduce variability into patient data. Recovery and progress cause changes in the patient motion data over the course of treatment. As well, fatigue and tiredness over the course of a session can change movement characteristics.
The presence of multiple other sources of variability makes the task of progress monitoring challenging.